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An Approach to Classify the Shot Selection by Batsmen in Cricket Matches Using Deep Neural Network on image Data

An Approach to Classify the Shot Selection by Batsmen in Cri...

引用

International conference on Computer and Information Technology (ICCIT)

作者： Afsana Khan Fariha Haque Nabila Masud Mohiuddin Mahadi Mollah Ashraful Alam Md Tanzim Reza Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

In recent times, technological advancement has brought a tremendous change in the field o f c ricket, which is a popular sport in many countries. Technology is being utilized to figure out projected score prediction, wicket prediction, winning probability, run rate, and many other parameters. In this research, our primary goal is to use Machine learning in the field of Cricket, where we aim to classify the shot played by the batsman, which can help in applications such as automated broadcasting systems or statistical data generation systems. For implementing our proposed model, we have generated our own dataset of cricket shot images by taking real-time photos from various cricket matches. We collected 1000 images of 10 different types of shots being played. For the classification task, we trained VGG-19 and Inception v3 model architecture and we got a better result by using VGG19. Before classification, the images had to go through several pre-processing methods such as background removal through Mask R-CNN, batsman segmentation through YOLO v3, etc. Then we used 83% of the total images to train the models and 17% to test the models. Finally, we achieved desired accuracy of 84.71% from VGG-19 and 82.35% from Inception-V3.

关键词： Deep learning image segmentation Computational modeling Neural networks Computer architecture Probability Real-time systems

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Deep Bayesian Multimedia Learning 20

Deep Bayesian Multimedia Learning

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28th ACM International conference on Multimedia (MM)

作者： Chien, Jen-Tzung Natl Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781450379885

Deep learning has been successfully developed as a complicated learning process from source inputs to target outputs in presence of multimedia environments. The inference or optimization is performed over an assumed deterministic model with deep structure. A wide range of temporal and spatial data in language and vision are treated as the inputs or outputs to build such a domain mapping for multimedia applications. A systematic and elaborate transfer is required to meet the mapping between source and target domains. Also, the semantic structure in natural language and computer vision may not be well represented or trained in mathematical logic or computer programs. The distribution function in discrete or continuous latent variable model for words, sentences, images or videos may not be properly decomposed or estimated. The system robustness to heterogeneous environments may not be assured. This tutorial addresses the fundamentals and advances in statistical models and neural networks for domain mapping, and presents a series of deep Bayesian solutions including variational Bayes, sampling method, Bayesian neural network, variational auto-encoder (VAE), stochastic recurrent neural network, sequence-to-sequence model, attention mechanism, end-to-end network, stochastic temporal convolutional network, temporal difference VAE, normalizing flow and neural ordinary differential equation. Enhancing the prior/posterior representation is addressed in different latent variable models. We illustrate how these models are connected and why they work for a variety of applications on complex patterns in language and vision. The word, sentence and image embeddings are merged with semantic constraint or structural information. Bayesian learning is formulated in the optimization procedure where the posterior collapse is tackled. An informative latent space is trained to incorporate deep Bayesian learning in various information systems.

关键词： deep learning Bayesian learning sequential learning multimedia domain mapping natural language processing computer vision

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Evaluating Precision and Context in image Captions

Evaluating Precision and Context in Image Captions

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Chaitrali Prasanna Chaudhari Satish Devane Lokamanya Tilak College of Engineering Koparkhairane Sector 4 Vikas Nagar Navi Mumbai Maharashtra Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering Gangapur Road Nashik Maharashtra

ISBN: (纸本)9781665405218;9781665446341

Captioning images is the process of generating the image description in a textual format that has wide applications in various domains. The research in this area is ongoing and seeking increasing attention as it merges the fields of computer vision and natural language processing. In the presented work, an image caption generation model consisting of a probabilistic and neural framework is built following an encoder-decoder scheme. The image is inputted to the deep learning classifier called Convolutional Neural Network (CNN) and to generate a set of sentences, a Long-Short Term Memory (LSTM) model is used. The captions generated by the proposed captioning model are compared over the other conventional models with respect to statistical analysis using standard and popular similarity metrics called Cosine Similarity, Precision and Recall. The results of evaluation of the predicted image captions compared to the actual captions are presented and the applicability of evaluation methods in context of image captioning is discussed and justified.

关键词： Measurement Deep learning Analytical models Computer vision statistical analysis Computational modeling Probabilistic logic

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Effective recognition of pollen grains based on parametric adaptation of the image identification model

Effective recognition of pollen grains based on parametric a...

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2020 International conference on Information Science and Communications Technologies, ICISCT 2020

作者： Ibragimovich, Jumanov Isroil Isroilovich, Djumanov Olimjon Maxmudovich, Xolmonov Sunatillo Samarkand State University Head of the Department of Information Technologies Samarkand Uzbekistan Samarkand State University Department of Information Technologies Samarkand Uzbekistan

ISBN: (纸本)9781728199696

Constructive approaches, principles and methods of image processing in conditions of a priori insufficiency, parametric uncertainty, low accuracy of information have been developed on the basis of statistical, dynamic, neural network models for identifying micro-objects. methods of point and nonlinear verification of the correspondence of the contours of the input and reference objects based on the selection, segmentation, interpolation of specific characteristics of images of pollen grains and regulation of raster parameters are proposed. Modified computational schemes for learning neural networks with forward and backward propagation of errors, regulation of interneural connections in layers, neuron weights, variable activation functions, network architecture, training samples, superposition of continuous input-output dependencies. A computer complex for identification in the C++ language in the 'CUDA' parallel computing environment has been implemented for the recognition, classification and systematization of pollen grains [1]. © 2020 IEEE.

关键词： Classification (of information)

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Neighborhood-aware autoencoder for missing value imputation

Neighborhood-aware autoencoder for missing value imputation

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European Signal processing conference (EUSIPCO)

作者： Helena Aidos Pedro Tomás LASIGE Universidade de Lisboa Lisbon Portugal INESC-ID Universidade de Lisboa Lisbon Portugal

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

Missing values are a fundamental issue in many applications by constraining the application of different learning methods or by impairing the attained results. Many solutions have been proposed by relying on statistical or machine learning techniques. However, in most cases, the results are not yet satisfactory. Hence, motivated by the advent of deep learning, different solutions have also been proposed, such as by adopting autoencoders and adversarial training. However, in most of these solutions, the results are impaired by the network structure and training strategy, constraining the accuracy of missing value imputation. In this paper, we revisit autoencoder networks and show that through a careful selection of network structure and optimization strategy we outperform other deep learning solutions. We further study the impact of a previously proposed technique, stochastic corruption of inputs, to show that when the network is well designed and trained, it actually impairs the results.

关键词： Deep learning Training Learning systems Europe Signal processing Tuning Optimization

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statistical analysis of the QTMT structure: Intra mode decision 4

Statistical analysis of the QTMT structure: Intra mode decis...

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4th IEEE International conference on image processing, Applications and Systems, IPAS 2020

作者： Zouidi, Naima Kessentini, Amina Belghith, Fatma Masmoudi, Nouri Laboratory of Electronics and Information Technology National Engineering School of Sfax Sfax Tunisia

ISBN: (纸本)9781728175744

As part of their contribution to the continuous development of video technologies, International Telecommunication Union (ITU) and Moving Picture Experts Group (MPEG) created the Joint Expert Team (JVET) in order to study the potential need for the standardization of the future video coding, called Versatile Video Coding (VVC). The VVC project was aiming at outperforming the coding efficiency of the High-Efficiency Video Coding (HEVC) by 40-50% with the same perceptual quality. With the release of its final version in July 2020, VVC has demonstrated a superior coding efficiency, up to 40% [6] of bit reduction beyond HEVC. However, it introduces significant computational complexity in the encoding engine. Such complexity should be analyzed. In this paper, the distribution of intra modes is illustrated through statistical analysis in order to speed up the intra mode decision process in the VVC. Experimental results show that the VVC uses the 6 Most Probable Modes (MPM) as the most used intra modes. In addition, these commonly used intra modes are usually numerous on deeper Multi-Type Tree (MT) depths unrelatedly to the Quantization Parameter (QP) but they may vary according to the video texture characteristics and the used intra coding tool, whether Multi Refrence Lines (MRL), Matrix Weighted Intra Prediction (MIP) or Intra Sub Partitions (ISP). This work can be considered as a milestone for proposing intelligent algorithms to perform fast encoding decisions in the intra prediction step. © 2020 IEEE.

关键词： statistical methods

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Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species

Intelligent Classification Procedure for Plasmodium Knowlesi...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International conference on

作者： Siti Nurul Aqmariah Bt Mohd Kanafiah Mohd Yusoff Mashor Zeehaida Mohamed Yessi Jusman Hasimah Bt Ali Nordiana Shariffudin Siti Marhainis Bt Othman Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia Department of Microbiology and Parasitology School of Medical Sciences Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9781665465427

Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.

关键词： Training White blood cells Electrical engineering image segmentation Shape Microscopy Feature extraction

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

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Proceedings of the 2022 11th International conference on Computing and Pattern Recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.

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Tuberculosis Bacilli Identification: A Novel Feature Extraction Approach via statistical Shape and Color Models 19

Tuberculosis Bacilli Identification: A Novel Feature Extract...

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19th IEEE International conference on Machine Learning and Applications, ICMLA 2020

作者： Yousefi, Hamed Mohammadi, Farnaz Mirian, Niloufar Amini, Navid Washington Univ. in St. Louis Dept. of Imaging Science St. LouisMO United States Ucla Dept. of Bioengineering Los AngelesCA United States Yale University Dept. of Radiology New HavenCT United States Cal. State Univ. Los Angeles Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9781728184708

Tuberculosis (TB) is an infectious disease resulting in 1.5 M deaths worldwide every year. Early diagnosis can play an important role in reducing TB induced mortality rates. A common inexpensive approach to test for TB is a manual examination of sputum smear images under the light/fluorescent microscope. To enhance the process, computer-aided diagnostic devices and machine learning algorithms have been developed with the capability of identifying TB bacilli using image processing techniques. Here, we propose a novel statistical model of the shape and color of TB bacilli in Ziehl-Neelsen stained images under the light microscope to identify bacilli in these images. These simple statistical models are utilized as a general library for reconstructing any bacillus with various background colors and can overcome the difficulties associated with geometric feature extraction methods. We use various methods to classify the individual bacilli and overlapping bacilli from the rest of the image based on the eigenvalues of the shape and color models. Our results show that the k-NN classifier performs best among selected classifiers with an average accuracy of 82.7% for single bacilli and overlapping bacilli detection, and 99.1% accuracy of identifying individual bacilli alone from overlapping bacilli and other objects. © 2020 IEEE.

关键词： image enhancement

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Detection of Estrus in Cattle by using image Technology and Machine Learning methods 9

Detection of Estrus in Cattle by using Image Technology and ...

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9th IEEE Global conference on Consumer Electronics, GCCE 2020

作者： Noe, Su Myat Zin, Thi Thi Tin, Pyke Hama, Hiromitsu University of Miyazaki Graduate School of Engineering Miyazaki Japan Osaka City University Graduate School of Engineering Osaka Japan

ISBN: (纸本)9781728198026

Detection of estrus in cattle in early phase is especially vital in the era of precision farming. This paper focuses on the detection of estrus in cattle by using image processing techniques and machine learning methods. In doing so, we first utilize an image analysis to investigate some behaviors of cattle in estrus, which is standing when mounted by the other cattle. We then extract some statistical measures based on polyline shape features of detected cattle images and utilize these measures as an input to machine learning algorithms. Specifically, in this paper, we employ the three supervised machine learning methods, which is Support Vector Machine (SVM), Logistic Regression (LR), and Multiple Linear Regression (MLR) classifiers. Some experimental works are performed by using real-life video sequences. The results show promising and capable to detect the behavior of estrus both cost-effectively (only image) and specifically with the detection rate of SVM is 97%, LR is 94%, and MLR is 94%, respectively. © 2020 IEEE.

关键词： Feature extraction

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